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  • In this chapter two deformable registration methods were described: 1) a block matching technique based on parametric transformations with radial basis functions and 2) a high dimensional registration technique with nonparametric deformation models based on spatial smoothing. The use of multimodal similarity measures was insisted. The multimodal character of the methods make them robust to tissue intensity variations which can be result of multimodality imaging as well as neuropsychological diseases or even normal aging. One of the described algorithms was demonstrated in the field of computational neuroanatomy, particularly for fully automated spatial detection of anatomical abnormalities in first episode and chronic schizophrenia based on 3-D MRI brain scans.
  • In this chapter two deformable registration methods were described: 1) a block matching technique based on parametric transformations with radial basis functions and 2) a high dimensional registration technique with nonparametric deformation models based on spatial smoothing. The use of multimodal similarity measures was insisted. The multimodal character of the methods make them robust to tissue intensity variations which can be result of multimodality imaging as well as neuropsychological diseases or even normal aging. One of the described algorithms was demonstrated in the field of computational neuroanatomy, particularly for fully automated spatial detection of anatomical abnormalities in first episode and chronic schizophrenia based on 3-D MRI brain scans. (en)
Title
  • Methods fo Nonlinear Intersubject registration in Neuroscience
  • Methods fo Nonlinear Intersubject registration in Neuroscience (en)
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  • Methods fo Nonlinear Intersubject registration in Neuroscience
  • Methods fo Nonlinear Intersubject registration in Neuroscience (en)
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  • RIV/65269705:_____/09:#0000825!RIV10-MZ0-65269705
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  • image registration; computational neuroanatomy; mutual information (en)
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  • Kašpárek, Tomáš
  • Schwarz, Daniel
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  • In-Tech
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  • 978-953-307-002-5
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